Exploiting Hierarchical Domain Values in Classification Learning ∗

parameter estimation, classification We propose a framework which can exploit hierarchical structures of feature domain values to improve classification performance. Mean-variance analysis method under this framework is investigated. One characteristic of our framework is that it provides a principled way to transform an original feature domain value to a coarser granularity by utilizing the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation in classification learning. We have conducted an experiment using a biological data set and. The results demonstrate that utilizing domain value hierarchies gains benefits for classification.

Exploiting Hierarchical Domain Values in Classification Learning ∗

parameter estimation, classification We propose a framework which can exploit hierarchical structures of feature domain values to improve classification performance. Mean-variance analysis method under this framework is investigated. One characteristic of our framework is that it provides a principled way to transform an original feature domain value to a coarser granularity by utilizing the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation in classification learning. We have conducted an experiment using a biological data set and. The results demonstrate that utilizing domain value hierarchies gains benefits for classification.